Jul 2026· Journal of Computer Virology and Hacking Techniques· Vol 22· 0 citations· 16 references
Computer Science
TL;DR
The results have shown how well-engineered static features coupled with overfitting-aware ensemble design can give robust results of multi-class malware classification in the absence of dynamic traces, to develop additional resilience from obfuscation and runtime-evasive threats.
The swift growth in the quantity of Android applications has resulted in a similar increase in the threat of malware, compromising the privacy and security of users. This research introduces a framework aimed at efficiently identifying malware within Android applications through machine learning techniques. Various cla...
S. Ramya, Baratam Sahith, Bokka Teenanjani et al.· 2026 4th International Confe...· 0 citations
Despite outstanding results, machine learning-based Android malware detection models struggle with concept drift, where rapidly evolving malware characteristics degrade model effectiveness. This study examines the impact of concept drift on Android malware detection, evaluating two datasets and nine machine learning an...
Ahmed Sabbah, Mohammed F. Kharma, Radi Jarrar et al.· Journal of Cybersecurity and...· 1 citation
FUADroid is proposed, a static malware detection method that fuses structural and statistical-semantic views and achieves strong detection performance and exhibits improved robustness under cross-year evaluation settings.
Jiyun Yang, Fan Mei, Zheng-Dong Wan et al.· International Journal of Mac...· 0 citations
An intelligent Android malware detection framework that combines deep learning with the Equilibrium Optimizer to improve detection performance is presented, providing an effective and reliable solution for securing Android devices against evolving malware threats.
Aishwarya Eklar, G.Rajini· International Journal of Eng...· 0 citations
The results confirm that hybrid resampling combined with optimized gradient boosting improves classification reliability, especially in addressing severe class imbalance and enhancing recognition capability across diverse Android malware families.
Ali Nur Ikhsan, Adam Prayogo Kuncoro, Debby Ummul Hidayah et al.· Journal of Information Syste...· 0 citations
Android malware has become a significant cybersecurity threat due to the open nature of the Android platform. To address this issue, the present study proposes a hybrid malware detection model that combines static and dynamic features with feature selection and ensemble learning. Three feature selection methods, includ...
H. Alharbi, R. Marie· Engineering, Technology &...· 0 citations
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